

import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


lowerCAmelCase				:    Any        =		trt.Logger(trt.Logger.WARNING)
lowerCAmelCase				:    str        =		absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

lowerCAmelCase				:    Optional[int]        =		logging.getLogger(__name__)

lowerCAmelCase				:    Any        =		argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    """--onnx_model_path""",
    default=None,
    type=str,
    required=True,
    help="""Path to ONNX model: """,
)

parser.add_argument(
    """--output_dir""",
    default=None,
    type=str,
    required=True,
    help="""The output directory where the model checkpoints and predictions will be written.""",
)

# Other parameters

parser.add_argument(
    """--tokenizer_name""",
    default="""""",
    type=str,
    required=True,
    help="""Pretrained tokenizer name or path if not the same as model_name""",
)

parser.add_argument(
    """--version_2_with_negative""",
    action="""store_true""",
    help="""If true, the SQuAD examples contain some that do not have an answer.""",
)
parser.add_argument(
    """--null_score_diff_threshold""",
    type=float,
    default=0.0,
    help="""If null_score - best_non_null is greater than the threshold predict null.""",
)

parser.add_argument(
    """--max_seq_length""",
    default=384,
    type=int,
    help=(
        """The maximum total input sequence length after WordPiece tokenization. Sequences """
        """longer than this will be truncated, and sequences shorter than this will be padded."""
    ),
)
parser.add_argument(
    """--doc_stride""",
    default=128,
    type=int,
    help="""When splitting up a long document into chunks, how much stride to take between chunks.""",
)

parser.add_argument("""--per_device_eval_batch_size""", default=8, type=int, help="""Batch size per GPU/CPU for evaluation.""")

parser.add_argument(
    """--n_best_size""",
    default=20,
    type=int,
    help="""The total number of n-best predictions to generate in the nbest_predictions.json output file.""",
)
parser.add_argument(
    """--max_answer_length""",
    default=30,
    type=int,
    help=(
        """The maximum length of an answer that can be generated. This is needed because the start """
        """and end predictions are not conditioned on one another."""
    ),
)

parser.add_argument("""--seed""", type=int, default=42, help="""random seed for initialization""")

parser.add_argument(
    """--dataset_name""",
    type=str,
    default=None,
    required=True,
    help="""The name of the dataset to use (via the datasets library).""",
)
parser.add_argument(
    """--dataset_config_name""",
    type=str,
    default=None,
    help="""The configuration name of the dataset to use (via the datasets library).""",
)
parser.add_argument(
    """--preprocessing_num_workers""", type=int, default=4, help="""A csv or a json file containing the training data."""
)
parser.add_argument("""--overwrite_cache""", action="""store_true""", help="""Overwrite the cached training and evaluation sets""")
parser.add_argument(
    """--fp16""",
    action="""store_true""",
    help="""Whether to use 16-bit (mixed) precision instead of 32-bit""",
)
parser.add_argument(
    """--int8""",
    action="""store_true""",
    help="""Whether to use INT8""",
)

lowerCAmelCase				:    Union[str, Any]        =		parser.parse_args()

if args.tokenizer_name:
						lowerCAmelCase				:    Optional[Any]        =		AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
						raise ValueError(
						    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
						    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""
						)

logger.info("""Training/evaluation parameters %s""", args)

lowerCAmelCase				:    List[str]        =		args.per_device_eval_batch_size

lowerCAmelCase				:    Optional[Any]        =		(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowerCAmelCase				:    Union[str, Any]        =		True

lowerCAmelCase				:    int        =		'temp_engine/bert-fp32.engine'
if args.fpaa:
						lowerCAmelCase				:    Optional[Any]        =		'temp_engine/bert-fp16.engine'
if args.inta:
						lowerCAmelCase				:    int        =		'temp_engine/bert-int8.engine'

# import ONNX file
if not os.path.exists("""temp_engine"""):
						os.makedirs("""temp_engine""")

lowerCAmelCase				:    Any        =		1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
						with open(args.onnx_model_path, """rb""") as model:
												if not parser.parse(model.read()):
																		for error in range(parser.num_errors):
																								print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
						lowerCAmelCase				:    str        =		[network.get_input(i) for i in range(network.num_inputs)]
						lowerCAmelCase				:    Dict        =		[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

						with builder.create_builder_config() as config:
												lowerCAmelCase				:    str        =		1 << 50
												if STRICT_TYPES:
																		config.set_flag(trt.BuilderFlag.STRICT_TYPES)
												if args.fpaa:
																		config.set_flag(trt.BuilderFlag.FPaa)
												if args.inta:
																		config.set_flag(trt.BuilderFlag.INTa)
												lowerCAmelCase				:    Dict        =		builder.create_optimization_profile()
												config.add_optimization_profile(profile)
												for i in range(len(input_names)):
																		profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
												lowerCAmelCase				:    str        =		builder.build_engine(network, config)

												# serialize_engine and store in file (can be directly loaded and deserialized):
												with open(engine_name, """wb""") as f:
																		f.write(engine.serialize())







def      A_ (       _UpperCAmelCase							,      _UpperCAmelCase							,      _UpperCAmelCase							,      _UpperCAmelCase							,      _UpperCAmelCase							,      _UpperCAmelCase							,      _UpperCAmelCase							,      _UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Tuple    						=			np.asarray(inputs["input_ids"]							,      dtype=np.intaa       )
						SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			np.asarray(inputs["attention_mask"]							,      dtype=np.intaa       )
						SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			np.asarray(inputs["token_type_ids"]							,      dtype=np.intaa       )

						# Copy inputs
						cuda.memcpy_htod_async(d_inputs[0]							,      input_ids.ravel()							,      _lowerCamelCase       )
						cuda.memcpy_htod_async(d_inputs[1]							,      attention_mask.ravel()							,      _lowerCamelCase       )
						cuda.memcpy_htod_async(d_inputs[2]							,      token_type_ids.ravel()							,      _lowerCamelCase       )
						# start time
						SCREAMING_SNAKE_CASE_:					int    						=			time.time()
						# Run inference
						context.execute_async(
						    bindings=[int(_lowerCamelCase       ) for d_inp in d_inputs] + [int(_lowerCamelCase       ), int(_lowerCamelCase       )]							,      stream_handle=stream.handle       )
						# Transfer predictions back from GPU
						cuda.memcpy_dtoh_async(_lowerCamelCase							,      _lowerCamelCase							,      _lowerCamelCase       )
						cuda.memcpy_dtoh_async(_lowerCamelCase							,      _lowerCamelCase							,      _lowerCamelCase       )
						# Synchronize the stream and take time
						stream.synchronize()
						# end time
						SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			time.time()
						SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			end_time - start_time
						SCREAMING_SNAKE_CASE_:					Tuple    						=			(h_outputa, h_outputa)
						# print(outputs)
						return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
lowerCAmelCase				:    List[Any]        =		Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
						datasets.utils.logging.set_verbosity_warning()
						transformers.utils.logging.set_verbosity_info()
else:
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
						set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
						# Downloading and loading a dataset from the hub.
						lowerCAmelCase				:    str        =		load_dataset(args.dataset_name, args.dataset_config_name)
else:
						raise ValueError("""Evaluation requires a dataset name""")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

lowerCAmelCase				:    Optional[int]        =		raw_datasets['validation'].column_names

lowerCAmelCase				:    Optional[int]        =		'question' if 'question' in column_names else column_names[0]
lowerCAmelCase				:    Dict        =		'context' if 'context' in column_names else column_names[1]
lowerCAmelCase				:    List[Any]        =		'answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
lowerCAmelCase				:    List[str]        =		tokenizer.padding_side == 'right'

if args.max_seq_length > tokenizer.model_max_length:
						logger.warning(
						    f'''The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'''
						    f'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''
						)

lowerCAmelCase				:    Optional[int]        =		min(args.max_seq_length, tokenizer.model_max_length)







def      A_ (       _UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Any    						=			[q.lstrip() for q in examples[question_column_name]]

						# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
						# in one example possible giving several features when a context is long, each of those features having a
						# context that overlaps a bit the context of the previous feature.
						SCREAMING_SNAKE_CASE_:					List[str]    						=			tokenizer(
						    examples[question_column_name if pad_on_right else context_column_name]							,      examples[context_column_name if pad_on_right else question_column_name]							,      truncation="only_second" if pad_on_right else "only_first"							,      max_length=_lowerCamelCase							,      stride=args.doc_stride							,      return_overflowing_tokens=_lowerCamelCase							,      return_offsets_mapping=_lowerCamelCase							,      padding="max_length"							,      )

						# Since one example might give us several features if it has a long context, we need a map from a feature to
						# its corresponding example. This key gives us just that.
						SCREAMING_SNAKE_CASE_:					Dict    						=			tokenized_examples.pop("overflow_to_sample_mapping"       )

						# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
						# corresponding example_id and we will store the offset mappings.
						SCREAMING_SNAKE_CASE_:					str    						=			[]

						for i in range(len(tokenized_examples["input_ids"]       )       ):
												# Grab the sequence corresponding to that example (to know what is the context and what is the question).
												SCREAMING_SNAKE_CASE_:					Tuple    						=			tokenized_examples.sequence_ids(_lowerCamelCase       )
												SCREAMING_SNAKE_CASE_:					Dict    						=			1 if pad_on_right else 0

												# One example can give several spans, this is the index of the example containing this span of text.
												SCREAMING_SNAKE_CASE_:					str    						=			sample_mapping[i]
												tokenized_examples["example_id"].append(examples["id"][sample_index]       )

												# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
												# position is part of the context or not.
												SCREAMING_SNAKE_CASE_:					Optional[int]    						=			[
												    (o if sequence_ids[k] == context_index else None)
												    for k, o in enumerate(tokenized_examples["offset_mapping"][i]       )
												]

						return tokenized_examples


lowerCAmelCase				:    Optional[int]        =		raw_datasets['validation']
# Validation Feature Creation
lowerCAmelCase				:    List[str]        =		eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="""Running tokenizer on validation dataset""",
)

lowerCAmelCase				:    Any        =		default_data_collator

lowerCAmelCase				:    Tuple        =		eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
lowerCAmelCase				:    int        =		DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)







def      A_ (       _UpperCAmelCase							,      _UpperCAmelCase							,      _UpperCAmelCase							,      _UpperCAmelCase="eval"       ):
						SCREAMING_SNAKE_CASE_:					int    						=			postprocess_qa_predictions(
						    examples=_lowerCamelCase							,      features=_lowerCamelCase							,      predictions=_lowerCamelCase							,      version_2_with_negative=args.version_2_with_negative							,      n_best_size=args.n_best_size							,      max_answer_length=args.max_answer_length							,      null_score_diff_threshold=args.null_score_diff_threshold							,      output_dir=args.output_dir							,      prefix=_lowerCamelCase							,      )
						# Format the result to the format the metric expects.
						if args.version_2_with_negative:
												SCREAMING_SNAKE_CASE_:					int    						=			[
												    {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
												]
						else:
												SCREAMING_SNAKE_CASE_:					Dict    						=			[{"id": k, "prediction_text": v} for k, v in predictions.items()]

						SCREAMING_SNAKE_CASE_:					str    						=			[{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
						return EvalPrediction(predictions=_lowerCamelCase							,      label_ids=_lowerCamelCase       )


lowerCAmelCase				:    Dict        =		load_metric("""squad_v2""" if args.version_2_with_negative else """squad""")

# Evaluation!
logger.info("""Loading ONNX model %s for evaluation""", args.onnx_model_path)
with open(engine_name, """rb""") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
						# setup for TRT inferrence
						for i in range(len(input_names)):
												context.set_binding_shape(i, INPUT_SHAPE)
						assert context.all_binding_shapes_specified







						def      A_ (       _UpperCAmelCase       ):
												return trt.volume(engine.get_binding_shape(_lowerCamelCase       )       ) * engine.get_binding_dtype(_lowerCamelCase       ).itemsize

						# Allocate device memory for inputs and outputs.
						lowerCAmelCase				:    Any        =		[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

						# Allocate output buffer
						lowerCAmelCase				:    Optional[int]        =		cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
						lowerCAmelCase				:    Dict        =		cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
						lowerCAmelCase				:    List[Any]        =		cuda.mem_alloc(h_outputa.nbytes)
						lowerCAmelCase				:    Optional[Any]        =		cuda.mem_alloc(h_outputa.nbytes)

						# Create a stream in which to copy inputs/outputs and run inference.
						lowerCAmelCase				:    Optional[int]        =		cuda.Stream()

						# Evaluation
						logger.info("""***** Running Evaluation *****""")
						logger.info(f'''  Num examples = {len(eval_dataset)}''')
						logger.info(f'''  Batch size = {args.per_device_eval_batch_size}''')

						lowerCAmelCase				:    Optional[int]        =		0.0
						lowerCAmelCase				:    Tuple        =		0
						lowerCAmelCase				:    str        =		timeit.default_timer()

						lowerCAmelCase				:    List[str]        =		None
						for step, batch in enumerate(eval_dataloader):
												lowerCAmelCase				:    int        =		model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
												total_time += infer_time
												niter += 1

												lowerCAmelCase				:    Union[str, Any]        =		outputs
												lowerCAmelCase				:    Tuple        =		torch.tensor(start_logits)
												lowerCAmelCase				:    List[str]        =		torch.tensor(end_logits)

												# necessary to pad predictions and labels for being gathered
												lowerCAmelCase				:    Any        =		accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
												lowerCAmelCase				:    Any        =		accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

												lowerCAmelCase				:    str        =		(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
												lowerCAmelCase				:    Optional[Any]        =		logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

						if all_preds is not None:
												lowerCAmelCase				:    Optional[Any]        =		nested_truncate(all_preds, len(eval_dataset))

						lowerCAmelCase				:    Optional[int]        =		timeit.default_timer() - start_time
						logger.info("""  Evaluation done in total %f secs (%f sec per example)""", evalTime, evalTime / len(eval_dataset))
						# Inference time from TRT
						logger.info("""Average Inference Time = {:.3f} ms""".format(total_time * 1000 / niter))
						logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1000))



						logger.info("""Total Number of Inference =  %d""", niter)

lowerCAmelCase				:    Optional[Any]        =		post_processing_function(eval_examples, eval_dataset, all_preds)
lowerCAmelCase				:    List[str]        =		metric.compute(predictions=prediction.predictions, references=prediction.label_ids)



logger.info(f'''Evaluation metrics: {eval_metric}''')








import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


lowercase    :   List[str]						=			logging.get_logger(__name__)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:Dict	,  *a						:Tuple	,  **a						:List[Any]		)						->					None:
								warnings.warn(
								    "The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
								    " Please use PerceiverImageProcessor instead."	,  a	,  )
								super().__init__(*a	,  **a		)